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High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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Published on: September 26, 2025

The Artificial Intelligence (AI) paradox.

Francesco Chiappelli1, Quyen French2, Allen Khakshooy3

  • 1Dental Group of Sherman Oaks, Sherman Oaks, CA 91403 & UCLA Center for the Health Sciences, Los Angeles, CA 90095.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) adoption is widespread but creates global inequalities. Increased AI use reveals its limitations in reliability, validity, and understanding human social skills.

Keywords:
AI markup language (AIML)AI-based clinical decision support systems (AI-CDSS)Artificial Intelligence (AI)ChatBotsChatGPTdeep learning (DP)deepfake technologygenerative AIgraphical processing units (GPUs)high-performance computing (HPC)machine learning (ML)natural language processing (NLP)tensor protocol units (TPUs)

Related Experiment Videos

Last Updated: May 12, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
06:11

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Published on: September 26, 2025

Area of Science:

  • Technology and Society
  • Artificial Intelligence Ethics
  • Global Digital Divide

Background:

  • Artificial intelligence (AI) adoption has surged globally across various sectors, from daily use to specialized medical applications.
  • Despite widespread AI utilization, significant inequalities persist, particularly widening the digital divide between the Northern and Southern hemispheres.
  • The increasing study and development of AI paradoxically uncover its inherent weaknesses, limitations, and potential societal dangers.

Purpose of the Study:

  • To examine the growing adoption of artificial intelligence (AI) worldwide.
  • To analyze the widening global inequalities in AI infrastructure and utilization.
  • To critically evaluate the limitations, reliability, and validity issues associated with AI development and deployment.

Main Methods:

  • Qualitative analysis of AI adoption trends and global disparities.
  • Review of critical perspectives on AI's societal impact and inherent risks.
  • Examination of AI's data-driven nature and its implications for information accuracy.

Main Results:

  • AI adoption is nearly universal but exacerbates the digital divide, with disparities between high-income and developing nations.
  • AI-generated information risks bias or inaccuracy due to data sources potentially lacking scientific validation.
  • AI demonstrates limitations in understanding complex human emotional and psychosocial skills, impacting its reliability and validity.

Conclusions:

  • The widespread growth of AI highlights significant challenges regarding its reliability and validity.
  • Addressing the ethical implications and inherent limitations of AI is crucial as its integration into society deepens.
  • Mitigating the global inequalities in AI access and development is essential for equitable technological advancement.